A groundbreaking study conducted by Ruishan Liu, WiSE Gabilan Assistant Professor of Computer Science at the University of Southern California (USC), sheds light on how unique genetic abnormalities affect cancer therapy outcomes. This study, published in Nature Communications, examined data from nearly 78,000 cancer patients representing 20 distinct cancer types, making it the largest of its kind. The results could greatly improve the precision and efficacy of cancer treatments.

The study discovered roughly 800 genetic alterations that have a direct impact on cancer patients' survival rates. Researchers discovered a strong correlation between 95 genes and survival in cancers, such as breast, ovarian, cutaneous, and gastrointestinal cancers. These insights could help doctors better personalise their treatments, thereby avoiding ineffective medications and focussing on ones that are more likely to work..

"By understanding how different mutations influence treatment response, doctors can select the most effective therapies," Ruishan Liu, a scientist, stated. "These discoveries highlight how genetic profiling can play a crucial role in personalised cancer care."

Genetic mutations (DNA alterations) can affect how cancer grows and how a patient reacts to treatment. Some mutations arise at random, while others are inherited. In cancer, these changes can influence whether a tumour is more aggressive or how it responds to specific treatments. Genetic testing is now widely utilised in cancer care to identify these mutations, allowing doctors to make more precise treatment decisions.

Patients with non-small cell lung cancer (NSCLC) are frequently tested for mutations in genes such as KRAS, EGFR, and ALK to assess whether targeted medicines or immunotherapies are beneficial.

The study provided important insights into therapy responses, notably in non-small cell lung cancer (NSCLC) and other malignancies. Widely seen in NSCLC, KRAS mutations have been associated with a lower response to EGFR inhibitors, highlighting the need for alternate therapeutic options. In contrast, NF1 mutations create a paradox: while they improve immunotherapy responses, they also reduce the efficacy of certain targeted therapies, demonstrating the complicated interplay of genetic factors in treatment planning.

Similarly, alterations in the PI3K pathway, which regulates cell proliferation, have cancer-specific effects. Variations in treatment responses have been observed in breast, melanoma, and kidney malignancies, highlighting the need for precision medicine tactics tailored to individual tumour profiles. Meanwhile, DNA repair pathway mutations have been shown to promote tumour instability in lung cancer, increasing the effectiveness of immunotherapy. This is consistent with data addressing immune-related pathway mutations, which link with increased survival rates in lung cancer patients undergoing immunotherapy, demonstrating that not all genetic abnormalities impede treatment success.

These findings highlight the relevance of genetic profiling in cancer therapy, which allows for a more targeted and personal approach to treatment. Understanding how diverse mutations affect drug reactions allows clinicians to optimise treatment regimens, improving patient outcomes while reducing ineffective interventions.

The study emphasises the necessity of precision medicine, which tailors treatment to each patient's unique genetic makeup. While there is a tremendous amount of mutation data, only a small number of medicines have been clinically verified, limiting the potential real-world impact and patient benefit. To close this gap, Liu's team employed machine learning to investigate how different mutations interact to affect treatment outcomes.

"Our goal was to find patterns that might not be obvious at first glance," Liu told me. "One key innovation lies in integrating huge amounts of data with advanced statistical and machine learning techniques to uncover previously unrecognised mutation-treatment interactions."

The researchers created a Random Survival Forest (RSF) model, which is a prediction tool used to refine therapy recommendations for lung cancer patients. By combining large-scale real-world data with machine learning, the model discovered novel mutation-treatment interactions. This technique has the potential to improve cancer patients' outcomes by increasing their access to immunotherapy.

Liu views this work as a significant step towards improving cancer therapy precision and personalisation, despite the need for more clinical trials to validate these findings. "This research shows the power of computational science in transforming complex clinical and genomic data into actionable insights," according to her. "It's deeply fulfilling to contribute to tools and knowledge that can directly improve patient care."